Experimental and early investigational drugs for androgenetic alopecia
Bibliographic record
Abstract
INTRODUCTION: Treatments for androgenetic alopecia constitute a multi-billion-dollar industry, however, currently available therapeutic options have variable efficacy. Consequently, in recent years small biotechnology companies and academic research laboratories have begun to investigate new or improved treatment methods. Research and development approaches include improved formulations and modes of application for current drugs, new drug development, development of cell-based treatments, and medical devices for modulation of hair growth. Areas covered: Here we review the essential pathways of androgenetic alopecia pathogenesis and collate the current and emerging therapeutic strategies using journal publications databases and clinical trials databases to gather information about active research on new treatments. Expert opinion: We propose that topically applied medications, or intra-dermal injected or implanted materials, are preferable treatment modalities, minimizing side effect risks as compared to systemically applied treatments. Evidence in support of new treatments is limited. However, we suggest therapeutics which reverse the androgen-driven inhibition of hair follicle signaling pathways, such as prostaglandin analogs and antagonists, platelet-rich plasma (PRP), promotion of skin angiogenesis and perfusion, introduction of progenitor cells for hair regeneration, and more effective ways of transplanting hair, are the likely near future direction of androgenetic alopecia treatment development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".